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检索条件"任意字段=Conference on Image Quality and System Performance XII"

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Transformer-based image Compression with Variable image quality Objectives

Transformer-based Image Compression with Variable Image Qual...

引用

Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and conference (APSIPA ASC)

作者： Kao, Chia-Hao Chen, Yi-Hsin Chien, Cheng Chiu, Wei-Chen Peng, Wen-Hsiao Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9798350300673

This paper presents a Transformer-based image compression system that allows for a variable image quality objective according to the user's preference. Optimizing a learned codec for different quality objectives leads to reconstructed images with varying visual characteristics. Our method provides the user with the flexibility to choose a trade-off between two image quality objectives using a single, shared model. Motivated by the success of prompt-tuning techniques, we introduce prompt tokens to condition our Transformer-based autoencoder. These prompt tokens are generated adaptively based on the user's preference and input image through learning a prompt generation network. Extensive experiments on commonly used quality metrics demonstrate the effectiveness of our method in adapting the encoding and/or decoding processes to a variable quality objective. While offering the additional flexibility, our proposed method performs comparably to the single-objective methods in terms of rate-distortion performance.

关键词： image compression

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From Raindrops To Pixels: A Novel Model to Predict ADAS Camera image Degradation in Rain

From Raindrops To Pixels: A Novel Model to Predict ADAS Came...

引用

IEEE International conference on Smart Mobility (SM)

作者： Li, Long Pao, Wing Yi Agelin-Chaab, Martin Komar, John Ontario Tech Univ Fac Engn & Appl Sci Oshawa ON Canada Ontario Tech Univ ACE Climat Aerodynam Wind Tunnel Oshawa ON Canada

ISBN: (纸本)9798350390148;9798350390131

The development of Advanced Driver Assistance systems (ADAS) has led to safer roads through the implementation of automated features such as blind spot monitoring, lane departure assist, and collision mitigation braking. In ADAS, cameras are one of the sensor types required to capture environmental information. However, camera performance degrades in adverse weather, especially in rain, due to droplet impacts, which cause changes to the light trajectory, thus decreasing object recognition and detection quality. While it is commonly understood that camera image quality directly correlates to rain intensity, understanding is limited in the exact relationship between incoming rain and image degradation in a dynamic environment due to the complexity of the multiphase interactions. In this study, a novel wind-driven rain system is deployed in conjunction with a spray-nozzle system at the ACE Climatic Aerodynamic Wind Tunnel. Using the controlled and repeatable testing methodology, objective camera image quality metrics are correlated with the incoming rain conditions through theoretical modeling. The model shows consistent trends across a variety of test conditions with different surface materials. The results prove the validity of the proposed model, which provides the impetus for further development to include additional physical interactions, such as droplet dynamics and vehicle aerodynamics.

关键词： camera sensor ADAS autonomous vehicle image quality rain weather wind tunnel soiling MSE PSNR multiphase

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Region of Interest Scalable image Compression Using Semantic Communications

Region of Interest Scalable Image Compression Using Semantic...

引用

2024 IEEE International conference on Consumer Electronics, ICCE 2024

作者： Samarathunga, Prabhath Gowrisetty, Vishnu Fernando, Thanuj Ganearachchi, Yasith Fernando, Anil University of Strathclyde Department of Computer and Information Sciences Glasgow United Kingdom

ISBN: (纸本)9798350324136

Growing consumer demand for media content across a wide range of devices has made scalable image compression vital in the current media landscape. image compression is conventionally achieved by means of statistical signal processing, but since recently deep learning techniques have been widely adopted as well. Capabilities of such systems also enable accurate identification of regions of interest in images, leading to optimized performance in most applications. This paper proposes a region-of-interest scalable image compression system using semantic communications, where an autoencoder-based semantic encoder performs the base-level compression, while a Semantic Mask Extracting Transformer (SeMExT) enables identification of regions of interest to create enhancement layers with different quality levels using a scalable JPEG encoder. When benchmarked against scalable JPEG across a variety of images, the proposed system demonstrates significantly improved compressive performance. The base layer achieved 61.4 times more compression on average, along with better rate-distortion performance at any given quality level. © 2024 IEEE.

关键词： Deep Neural Networks image Compression Region of Interest Scalable image Compression Semantic Communications

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Suppression and Simulation of Stray Radiation in Infrared Optical system 12

Suppression and Simulation of Stray Radiation in Infrared Op...

引用

conference on Optical Design and Testing xii

作者： Tian, Jianguo Shi, Mingqian Hou, Linbao Gao, Wen Rui, Jicai Zhou, Bin Shanghai Aerosp Control Technol Inst Shanghai 201109 Peoples R China Shanghai Key Lab Aerosp Intelligent Control Techn Shanghai 201109 Peoples R China

ISBN: (纸本)9781510656970;9781510656963

The sources of different spurious radiation in infrared optical system are analyzed, and the measures to suppress the spurious radiation are listed. Taking the infrared optical system of a detector as an example, the stray radiation of the infrared optical system is suppressed by setting the lyot stop. The optical and mechanical structure model of the infrared optical system was established, and the Monte Carlo method in LightTools software was used for ray tracing analysis. The illuminance of the image plane of the detector after adding lyot stop was obtained, and the point source transmittance (PST) and veiling glare index (V) of the infrared system were calculated, and the effectiveness of the suppression effect was evaluated. At the same time, the background heat radiation of the internal optical and mechanical structure of the infrared optical system is simulated and analyzed. The comprehensive consideration ensures that the imaging quality of the infrared optical system is not affected.

关键词： stray radiation lyot stop Monte Carlo point source transmittance veiling glare index background heat radiation

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A quality Evaluation Method for images in Adverse Weather Conditions 2

A Quality Evaluation Method for Images in Adverse Weather Co...

引用

2nd IEEE International conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： Shao, Zhengxi Chen, Jifeng Lin, Wenxin School of Information and Electromechanical Engineering Hunan International Economics University Changsha China

ISBN: (纸本)9798350373646

By adopting an adaptive image quality evaluation method under adverse weather conditions, an advanced image defogging system has been developed. This system aims to predict the optimal algorithm for achieving the best image defogging results. The system integrates nine efficient defogging algorithms. To comprehensively evaluate the defogging performance of these algorithms, a set of evaluation metrics including PSNR, SSIM, LPIPS, ENL, MSE, and RMSE is employed to establish a comprehensive evaluation method. By calculating the values of these evaluation metrics, the optimal algorithm suitable for the image is predicted, and the defogged image produced by the optimal algorithm is processed to output the best-defogged image finally. All predicted data is stored in a database. When defogging an image, if there is matching data in the database, the corresponding optimal algorithm and image processing are directly selected, thereby enhancing the overall defogging efficiency. © 2024 IEEE.

关键词： Algorithm Prediction Deep Learning Evaluation Method image Defogging system image Processing

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image Noise Compensation in Industrial Implementation of the SFF Method Using Controllable Optics 25

Image Noise Compensation in Industrial Implementation of the...

引用

25th International Carpathian Control conference (ICCC)

作者： Lenty, Bartosz AGH Univ Krakow Fac Mech Engn & Robot Krakow Poland

ISBN: (纸本)9798350350708;9798350350715

To keep up with increasing production efficiency and rising consumer demands, the industry is increasingly implementing quality control systems. Vision systems are starting to play an increasingly important role in quality control. Such systems allow the assessment of the geometry and surface of a part in an autonomous and non-contact manner. 3D vision systems are a particular case of vision systems. 3D vision systems allow the measurement of a scene, including depth information. As a result, a broader analysis of dimensions and defects is possible which for 2D systems may remain neglected. Only a few 3D imaging methods are applicable in industrial conditions. Scanners based on laser triangulation, stereovision and time of flight technologies are available. This article discusses the industrial implementation of another 3D image acquisition method - Shape From Focus. Shape from focus estimates the scene depth by comparing local sharpness in the image during the movement of the image plane. Usually image plane position is changed by the movement of the vision system. The novel approach is the use of electrically controlled optics to replace the movement of the system. It recognizably improves 3D scene acquisition performance. Electrically controlled optics also introduces the undesirable implications of changing the focusing power and magnification during acquisition. This results in noise in the 3D image. The article proposes a compensation algorithm to reduce the impact of the mentioned factors on the final 3D image. The paper discusses the structure of the stand used in the research. Factors affecting the quality of the 3D image obtained are presented and compensation algorithm is proposed. The study focuses on images of actual industrial materials.

关键词： Shape-from-focus quality control 3D imaging noise industrial

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Design and Implementation of Adaptive Gaussian Denoising Algorithm Based on FPGA 7

Design and Implementation of Adaptive Gaussian Denoising Alg...

引用

7th International conference on Automation Electronics and Electrical Engineering

作者： Chen, Xinghao Beihang Univ Sch Elect Informat Engn Beijing Peoples R China

ISBN: (纸本)9798350377040;9798350377033

With the continuous progress of image processing and machine vision technology, the demand for efficient and real-time processing is becoming more and more prominent, especially in the field of high-noise image processing. In this study, an adaptive Gaussian filtering algorithm is proposed, which is implemented based on FPGA and aims to improve the computational efficiency and real-time performance of the image processing system. Compared with the traditional fixed-weight filter, this algorithm is able to dynamically adjust the filtering parameters according to different noise environments, effectively balancing noise suppression and image detail retention. We coded the algorithm using Verilog hardware description language and verified it on PYNQ-Z2 FPGA platform. The experimental results show that the adaptive algorithm outperforms the fixed-weight filtering method in terms of performance, especially in terms of noise suppression and detail preservation. Meanwhile, the FPGA hardware implements the reduction of filtering delay and optimization of resource consumption, making it well suited for real-time applications. This study demonstrates the promise of FPGA adaptive filtering for applications in medical imaging, remote sensing, and intelligent surveillance, which have stringent requirements for high-performance and high-efficiency processing. This research provides new hardware solutions for real-time, high-quality image processing in constrained environments.

关键词： FPGA image denoising gaussian filtering adaptive algorithm

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Tomographic Object Detection by Unsupervised Clustering 2

Tomographic Object Detection by Unsupervised Clustering

引用

2nd International conference on Computer Vision and Intelligent Technology

作者： Wang, Fengyuan Wang, Zheng Wu, Yimin Jiang, Yandan Ji, Haifeng Wang, Baoliang Zhejiang Univ State Key Lab Ind Control Technol Coll Control Sci & Engn Hangzhou Peoples R China

ISBN: (纸本)9798331540050;9798331540043

This work introduces clustering to electrical tomography (ET) and first compares the effectiveness of different unsupervised clustering categories in tomographic object detection. First, the one-step linear back projection (LBP) algorithm is applied to reconstruct a rough image. Then, clustering is introduced to detect and refine the objects in the image, and a higher-quality image is obtained based on the clustering results. Six unsupervised clustering algorithms belonging to different categories, including K-means, Mini Batch K-means, Agglomerative, density peak clustering (DPC), statistical information grid (STING), gaussian mixture model (GMM), are compared from the aspects of clustering evaluation index, reconstruction quality and reconstruction efficiency. Simulation was carried out based on a capacitively coupled electrical resistance tomography (CCERT) system to collect the projection data under the cases of one to three objects to be detected in the sensing region. The research results show that the quality of object detection and reconstruction can be effectively improved by post-processing the image with clustering. It is found that for the single-object distribution, the grid-based clustering algorithm STING provides images with the highest quality. While for the multi-objects distributions (two or three objects), the Agglomerative algorithm belonging to the hierarchy clustering category shows advantage in achieving good images. Concerning the real-time performance, Agglomerative clustering algorithm is also preferred with less computational time than most of the other clustering algorithms. Therefore, among the combinations investigated in this work, LBP + Agglomerative clustering has the overall best performance in topographic object detection, in regarding of the reconstruction quality and efficiency.

关键词： electrical tomography (ET) image reconstruction clustering object detection image post-processing

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Finger Vein image quality Assessment Based on Stochastic Embedding Robustness 42

Finger Vein Image Quality Assessment Based on Stochastic Emb...

引用

42nd Chinese Control conference, CCC 2023

作者： Yang, Chen Guo, Jian Zhang, Yibo Huang, Yanan Guo, Zijie School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing210023 China

ISBN: (纸本)9789887581543

Finger vein image quality assessment is used to evaluate the quality of images, namely to evaluate the applicability of the finger vein images to the recognition system. The work of quality assessment will largely affects the performance of the finger vein recognition system. In previous work, people usually design quality indicators manually, construct quality functions or perform supervised learning based on visual inspection, which has limitations of subjectivity. In order to solve these problems, this paper proposes a finger vein image quality assessment algorithm based on Stochastic Embedding Robustness (SER), which is called SER algorithm in this paper. The algorithm generates random subnetworks from the deployed finger vein recognition system, and indirectly evaluates the image quality by calculating the variation of different subnetworks. The simulation results show that, SER algorithm can effectively filter out low-quality images and improve the performance of the recognition system. Compared with other methods, it is more efficient and stable. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： image quality

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Research on image Signal Enhancement and Denoising Algorithms for Intelligent Communication systems

Research on Image Signal Enhancement and Denoising Algorithm...

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2024 International conference on Information Technology, Communication Ecosystem and Management, ITCEM 2024

作者： Luo, Shenghan Wang, Xinyan Southeast University Nanjing211189 China Xuzhou Pharmaceutical Vocational College Xuzhou221116 China

ISBN: (纸本)9798331542696

The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intelligent communication systems. For image enhancement, we designed an adaptive histogram equalization algorithm based on blocks and a contrast adaptive optimization method, and implemented a detail enhancement algorithm combining multi-scale edge detection and enhancement. In terms of image denoising, we proposed adaptive median filtering, improved soft-threshold wavelet domain denoising, and non-local means algorithms to effectively suppress various types of noise. The system is developed using a hybrid \mathrm{C}++ and Python framework with parallel processing achieved through multithreading technology. Experimental results show that the proposed algorithms significantly improve image quality, processing efficiency, and system stability, providing reliable image signal processing support for intelligent communication systems. © 2024 IEEE.

关键词： C++ (programming language)

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